Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use xiaoding/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiaoding/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xiaoding/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xiaoding/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("xiaoding/finetuning-sentiment-model-3000-samples") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bba0a165484da8d19cc309b22465872e2e0bb9294015ce84224317811902717f
- Size of remote file:
- 268 MB
- SHA256:
- 0b86ba440cb93457a755c4591b5c1ba08dac01fa779c95d8fe1a8a72e5e5bdf4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.